Agritrop
Accueil

Linking cocoa polyphenol composition to chocolate quality with Average-Mass-Spectra fingerprints

Fayeulle Noémie, Meudec Emmanuelle, Boulet Jean-Claude, Hue Clotilde, Boulanger Renaud, Cheynier Véronique, Sommerer Nicolas. 2019. Linking cocoa polyphenol composition to chocolate quality with Average-Mass-Spectra fingerprints. In : Cocotea 2019 book of abstracts. Jacobs University Bremen. Bremen : Jacobs University Bremen, Résumé, 4. International Congress on Cocoa Coffee and Tea. 5, Bremen, Allemagne, 26 Juin 2019/28 Juin 2019.

Communication sans actes
[img]
Prévisualisation
Version publiée - Anglais
Utilisation soumise à autorisation de l'auteur ou du Cirad.
ID593380.pdf

Télécharger (118kB) | Prévisualisation

Résumé : Approaches enabling prediction of chocolate quality from cocoa composition would avoid time-and money-consuming steps to chocolate makers. Average mass spectra of cocoa-polyphenol-extracts led to fingerprints used to select the molecules that discriminate chocolate sensory groups. 16 worldwide cocoa samples were processed into chocolates which were characterized by sensory analysis, allowing sorting of the samples into four sensory groups. The cocoa polyphenol extracts were analyzed by liquid chromatography−low-resolution mass spectrometry. Averaging each mass spectrum provided polyphenolic fingerprints, which were combined into a matrix and processed with chemometrics (PCA, PLS-DA) to select the most meaningful molecules for discrimination of the chocolate sensory groups[1]. A larger set af 44 cocoa samples was used to validate the previous results. 29 mass signals of known and unknown molecules, mainly flavan-3-ols, were finally targeted, including 2 newly described ethyl-bridged flavan-3-ols[2], enabling sensory-group discrimination. Average mass spectra fingerprints of cocoa-polyphenol-extracts proved to be quick and efficient to select the molecules that discriminate chocolate sensory groups. A targeted MRM (Multiple Reaction Monitoring) mass spectrometry method was then developed and validated to routinely analyse large series of cocoa samples.

Auteurs et affiliations

  • Fayeulle Noémie, INRA (FRA)
  • Meudec Emmanuelle, INRA (FRA)
  • Boulet Jean-Claude, INRA (FRA)
  • Hue Clotilde, Chocolaterie Valrhona (FRA)
  • Boulanger Renaud, CIRAD-PERSYST-UMR Qualisud (FRA) ORCID: 0000-0001-9396-5634
  • Cheynier Véronique, INRA (FRA)
  • Sommerer Nicolas, INRA (FRA)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/593380/)

Voir la notice (accès réservé à Agritrop) Voir la notice (accès réservé à Agritrop)

[ Page générée et mise en cache le 2024-11-04 ]